FER datasets can be broadly categorized as either controlled or in-the-wild. Controlled datasets are posed by trained actors and photographed in the lab with regular illumination. The extended Cohn-Kanade dataset (CK+; [1]}) is a classic example of a controlled dataset. It contains 593 video sequences from 123 individuals that start from neutral expression and culminate in the intended expression (one of seven categories: anger, contempt, disgust, fear, happiness, sadness, and surprise). In contrast, in-the-wild datasets are crawled from the web by searching for emotion-related keywords. This type of dataset is typically larger than controlled datasets, and noisier in terms of identity, illumination, etc. FER+ [2]}, a re-labeled version of FER2013 [3]}, has been a popular early dataset in the field, with 28,709 training images, 3,589 validation images and 3,589 test images, consisting of the same seven expressions as the CK+ dataset. Currently, AffectNet [4]} is the largest publicly-available labelled dataset on facial expressions (see Dataset section below for detailed information).
